Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

Recently, the demand for more precise and demand-oriented customized spatial information is increasing due to the 4th industrial revolution. In particular, the use of 3D spatial information and digital twins which based on spatial information, and research for solving social problems in cities by using such information are continuously conducted. Globally, non-face-to-face services are increasing due to COVID-19, and the national policy direction is also rapidly progressing digital transformation, digitization and virtualization of the Korean version of the New Deal, which means that 3D spatial information has become an important factor to support it. In this study, physical objects for cities defined by world organizations such as ISO, OGC, and ITU were selected and the target of the 3D object model was limited to buildings. Based on CityGML2.0, the data collected using a drone suitable for building a 3D model of a small area is selected to be updated through road name address and building ledger, which are administrative information related to this, and LoD2.5 data is constructed and urban space. It was intended to suggest an object update method for a 3D building among data.

This study explores multiple variables of an OTT service for discovering hidden relationship between rating and the other variables of each successful and failed content, respectively. In order to extract key variables that are strongly correlated to the rating across the contents, this work analyzes 170 Netflix original dramas and 419 movies. These contents are classified as success and failure by using the rating site IMDb, respectively. The correlation between the contents, which are classified via rating, and variables such as violence, lewdness and running time are analyzed to determine whether a certain variable appears or not in each successful and failure content. This study employs a regression analysis to discover correlations across the variables as a main analysis method. Since the correlation between independent variables should be low, check multicollinearity and select the variable. Cook's distance is used to detect and remove outliers. To improve the accuracy of the model, a variable selection based on AIC(Akaike Information Criterion) is performed. Finally, the basic assumptions of regression analysis are identified by residual diagnosis and Dubin Watson test. According to the whole analysis process, it is concluded that the more director awards exist and the less immatatable tend to be successful in movies. On the contrary, lower fear tend to be failure in movies. In case of dramas, there are close correlations between failure dramas and lower violence, higher fear, higher drugs.

The role of transient receptor potential vanilloid receptor-1 (TRPV1) has been primarily investigated in pain sensory neurons. Relatively, little research has been performed in testicular cells. TRPV1 is abundantly expressed in Leydig cells of young adult mice. This study was conducted to determine the role of the TRPV1 channel in Leydig cells. TRPV1 modulators and testosterone were treated to the mouse Leydig cell line TM3 cells for 24 h. Capsaicin, a TRPV1 activator, dose-dependently induced cell death, whereas capsazepine, a TRPV1 inhibitor, inhibited capsaicin-induced cell death. Testosterone treatment reduced capsaicin-induced cell death. High concentrations of testosterone decreased TRPV1 mRNA and protein expression levels. However, TRPV1 modulators did not affect testosterone production. These results showed that capsaicin induced cell death of Leydig cells and that testosterone reduced capsaicininduced cell death. Our findings suggest that testosterone may regulate the survival of Leydig cells in young adult mice by decreasing the expression level of TRPV1.

Increasing the efficiency of HR (homologous recombination) is important for a successful knock-in. Rad51 is mainly involved in homologous recombination and is associated with strand invasion. The HR-related mismatch repair system maintains HR fidelity by heteroduplex rejection and repair. Therefore, the purpose of this study is to control Rad51, which plays a critical role in HR, through UV-induced DNA damage. It is also to confirm the effect on the expression of MMR related genes (Msh2, Msh3, Msh6, Mlh1, Pms2) and HR-related genes closely related to HR through treatment with the MMR inhibitor CdCl2. The mRNA expression of Rad51 gene was confirmed in both HC11 cells and mouse testes, but the mRNA expression of Dmc1 gene was confirmed only in mouse testes. The protein expression of Rad51 and Dmc1 gene increased in UV-irradiated HC11 cells. After 72 hours of treatment with 1 μm of CdCl2, the mRNA expression level of Msh3, Pms2, and Rad51 decreased, but the mRNA expression level of Msh6 and Mlh1 increased in HC11 cells. There was no significant difference in Msh2 mRNA expression between CdCl2 untreated-group and the 72 hours treated group. In conclusion, HR-related gene (Rad51) was increased by UV-induced DNA damage. Treatment of the MMR inhibitor CdCl2 in HC11 cells decreased the mRNA expression of Rad51.

본 연구의 목적은 장애아동 양육자의 물리적·정서적 웰니스 사이에서 지적 웰니스의 매개 효과를 조사하여 효율적인 관리 방안 제시에 필요한 기초자료를 제공하기 위함이다. 연구 대상자는 장애 아동 양육자 177명이며, 자료수집은 2019년 06월부터 2019년 11월까지 하였다. 자료분석은 PROCESS macro를 이용하였다. 일반적인 특성과 변수간 분석은 Independent t-test 및 ANOVA를 통해 통계적으로 유의한 차이가 있는 변수에 대하여는 Scheffé 사후검증을 실시하였다. 변수간 관련성 분석은 Pearson 상관관계 및 위계 다중회귀분석을 하였다. 연구결과, 장애아동 주양육자의 물리적 웰니스와 지적 웰니스는 장애아동 양육자의 정서적 웰니스에 직접적인 영향을 미치며, 또한 물리적 웰니스와 정서적 웰니스 사이에서 지적 웰니스가 매개효과를 제공하고 있다. 이는 장애아동이 가진 물리적 환경의 영향도 중요하 지만, 주양육자에게 적절하고 충분한 지적 교육이 동반되어야 정서적 웰니스 증진이 이루어질 수 있다는 것을 발견하였다. 따라서 다양한 교육 프로그램의 확대는 장애아동 양육자의 웰니스 증진에 기여할 수 있을 것이다.

확산강조 영상은 자기공명영상에서 진단에 매우 중요한 역할을 차지하고 있다. 또한, 높은 b-value를 요구하는 검사가 늘어나는 추세로 확산강조 영상을 얻기 위하여 검사 시간이 길어지게 되며 인공물과 전체적인 신호 감소가 동반하게 된다. 본 연구에서는 확산강조 영상에서 확산 방법을 3스캔 추적 확산강조 영상(3-scan trace diffusion weighted image, tDWI) 에서 DTI의 원리를 적용한 대각선 확산강조 영상(diagonal diffusion weighted image, dDWI)로 변경하여 획득 하였을 때, 두 방법 간의 검사 시간의 단축과 영상을 비교 평가하였다. 농도가 0에서 50%까지 10% 간격인 팬텀을 제작하여 제한된 확산(restrict diffusion)을 단발 스핀에코평면영상(single shot spin echo echo planar, SE-EPI) 기법을 사용하여 검사하였다. 확산 방법을 변경하며 tDWI와 dDWI 검사를 진행하였으며, 획득한 영상을 Image J 프로그램을 이용 하여 농도별 동일한 관심 체적을 설정하여 신호를 측정 후 분석방법은 독립 T-test를 이용하고, 통계적 분석은 SAS를 통해 신호 대 잡음 비와 대조도 대 잡음 비를 비교·분석하였다. p-value 0.05를 기준으로 유의성을 검증하였다. b0를 제외한 b100 이상은 전반적으로 신호 대 잡음 비, 대조도 대 잡음 비가 크며, p-value 0.05 이상으로 나타났다. 확산 방법 간 유의하지 않지만, 전반적으로 dDWI이 tDWI에 비해 평균이 높았다. dDWI 방법이 tDWI에 비해 화질의 변화 없이 검사 시간이 18.35% 단축되는 효과가 있으며, 신호 대 잡음 비와 대조도 대 잡음 비가 소폭 향상된 것으로 나타나 다른 부위의 확산강조 영상을 사용하는 검사에 적용 시 유용할 것으로 사료 된다.

This study investigated the relative predictive power of vocabulary depth and reading fluency on the reading comprehension of advanced Korean EFL learners in college. By doing so, the scope of paradigmatic relations, as part of vocabulary depth, was extended to encompass associative vocabulary as well as synonyms and antonyms, and reading fluency at the discourse level was considered. For this study, 139 college students were tested on a range of vocabulary depth tests, as well as reading comprehension and text-level reading fluency. The findings revealed that although both vocabulary depth and reading fluency are significant contributors to reading comprehension abilities, the predictability of vocabulary depth was larger than that of reading fluency. In addition, associative vocabulary not only revealed additional predictive power for reading comprehension on top of reading fluency, synonyms and antonyms, but also showed stronger predictability compared to synonyms and antonyms. These results highlight that both vocabulary depth, especially the knowledge of how words are related together, and text-level reading fluency play a crucial role in boosting the reading comprehension abilities of even advanced L2 readers.